Organophosphorus compounds (OPC) are a large class of organic compounds that provide a wide range of applications, and their importance has grown steadily in recent years. In each category and family, these compounds have similarities and differences. Due to their immense variety, these chemicals have various properties and, therefore, various applications. In fact, various works have been published recently that present the main applications of OPC, especially in metal extraction. Despite their extemsive range of use, optimizing their performance as extractant agents remains a challenge due to their structural variability and sensitivity to process parameters. This review provides a critical analysis of pentavalent OPCs, focusing on how their chemical nature influences heavy metal extraction efficiency. For the first time, we present a novel classification system for OPCs based on phosphorus valency and heteroatom coordination, offering a framework to guide future research. Our findings reveal that the direct coordination of the phosphorus to heteroatoms such as oxygen, sulfur, and nitrogen has a great influence on the physicochemical characteristics of the extractant and the metal extraction efficiency. This observation is in line with Pearson’s Hard and Soft Acids and Bases (HSAB) theory in the sense that it demonstrates that altering the heteroatom alters the metal affinity of the ligand. As a result, these structural modifications can improve the extraction performance by up to 40% for some heavy metals, highlighting the potential for optimized molecular designs to maximize industrial applications. In the future, this work offers a solid foundation for future studies on the rational design of organophosphorus-based extractants. Using HSAB theory and our novel classification system, researchers can rationally design OPCs for their target metal with unparalleled precision. These results have transformative impacts on metal recovery efficiency-intensive sectors like mining, waste recycling, and clean energy technologies.

Hydrogen is recognized as a clean energy replacement for non-renewable fossil fuels, and the utilization of metal-organic frameworks (MOFs) for hydrogen storage has gained considerable interest in recent years. In this study, hydrogen storage in MOFs was estimated using white-box methods, namely group method of data handling (GMDH), genetic programming (GP), and gene expression programming (GEP), which are robust soft-computing methods known for generating innovative correlations. To this end, temperature, pressure, pore volume, and surface area were implemented as input parameters for constructing these robust correlations. After that, the superiority of the established correlations was demonstrated through multiple statistical and graphical error assessment. The results indicated, the GMDH model demonstrates the highest accuracy with root mean square error (RMSE), and mean absolute error (MAE) values of 0.410 and 0.307, respectively. However, the GEP model’s accuracy was comparable to that of the GMDH model. In addition, sensitivity assessment showed that the pore volume and the pressure exhibit the strongest linear and non-linear relationships, respectively, with the H₂ storage in MOFs. This was demonstrated by a Pearson correlation coefficient of 0.5 and a Spearman correlation coefficient of 0.56, respectively. Furthermore, temperature had a minimal negative impact on the H₂ storage in MOFs according to Pearson, Spearman, and Kendall coefficients. Finally, to confirm the findings of the GMDH model, the leverage approach was applied, demonstrating that 96% of the data falls within the acceptable region, confirming the statistical reliability of the developed models.